def detect_joints(frame_list):
joints_dir_results = utils.get_pwd() + "/our_results/joints"
file = open(joints_dir_results + '/coco/shoulder_xy.txt', 'w') # For
file.write(
"Frame #n: Right shoulder (Point 2), Right elbow (Point 3), Left shoulder (Point 5), Left elbow (Point 6) \n"
)
for i in range(len(frame_list)):
# Find joints
frame = frame_list[i]
# Based on MPI DB
# keypoints, skeleton = OpenPoseImageREL.get_pose(frame.copy(), "MPI")
# cv2.imwrite(joints_dir_results + '/mpi/' + str(i) + '_keypoints.jpg', keypoints)
# Based on COCO DB
keypoints, skeleton, shoulder_points = OpenPoseImageREL.get_pose(
frame.copy(), "COCO")
cv2.imwrite(joints_dir_results + '/coco/' + str(i) + '_keypoints.jpg',
keypoints)
# Write result to file
RS = shoulder_points[0] if (len(shoulder_points) > 0) else (-1, -1)
RE = shoulder_points[1] if (len(shoulder_points) > 1) else (-1, -1)
LS = shoulder_points[2] if (len(shoulder_points) > 2) else (-1, -1)
LE = shoulder_points[3] if (len(shoulder_points) > 3) else (-1, -1)
file.write("%s: %s, %s, %s, %s \n" % (i, RS, RE, LS, LE))
file.close()
def get_extra_point_lists():
inf = open(utils.get_pwd() + '/our_results/joints/coco/shoulder_xy.txt')
frames_lists = list()
first = True
for line in inf:
if first is True:
first = False
continue
loc_list = list()
regex = re.findall('[0-9]+, [0-9]+', line)
for r in regex:
regex2 = re.split(', ', r)
x = int(regex2[0])
y = int(regex2[1])
loc_list.append((x, y))
frames_lists.append(loc_list)
return frames_lists
return frames_lists
def combine_feature_lists(feature_list1, feature_list2):
combined_frames_list = list()
for i in range(len(feature_list1)):
combined_list = list()
combined_list.extend(feature_list1[i])
combined_list.extend(feature_list2[i])
combined_frames_list.append(combined_list)
return combined_frames_list
if __name__ == "__main__":
full_frame_list = utils.get_all_frames(utils.get_pwd() +
'/our_data/ariel.avi')
full_frame_mask_list = utils.get_all_frames(utils.get_pwd() +
'/our_data/mask.avi')
texture = cv2.imread(utils.get_pwd() + '/our_data/style.jpg')
rows, cols, _ = np.shape(full_frame_list[0])
background = cv2.imread(utils.get_pwd() + '/our_data/starry_bg.jpg')
# background = background[:rows, :cols, :]
bg_motion_list = calc_bg_motion_frames(background)
new_bg_motion_list = list()
for bg_frame in bg_motion_list:
for i in range(10):
new_bg_motion_list.append(bg_frame)
def detect_features(frame_list):
pwd = utils.get_pwd()
# Input
haar_dir = pwd + "/haar_xmls"
faceCascPath = haar_dir + "/haarcascade_frontalface_default.xml"
eyeCascPath = haar_dir + "/haarcascade_eye.xml"
noseCascPath = haar_dir + "/Nariz_nose.xml"
mouthCascPath = haar_dir + "/Mouth.xml"
# Output
haar_dir_results = pwd + "/our_results/haar_detection"
utils.clean_output_directories(haar_dir_results)
# Construct the haar cascades
faceCascade = cv2.CascadeClassifier(faceCascPath)
eyeCascade = cv2.CascadeClassifier(eyeCascPath)
noseCascade = cv2.CascadeClassifier(noseCascPath)
mouthCascade = cv2.CascadeClassifier(mouthCascPath)
# Counters
no_nose_cnt = 0
bad_frames_cnt = 0
very_bad_frames_cnt = 0
frames_features = list()
for i in range(len(frame_list)):
# Read the image
image = frame_list[i]
image_dup = image.copy()
# For drawing and showing results, without changing original image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect features in the image
# feature_params = dict(maxCorners=gray.size, qualityLevel=0.05, minDistance=5, blockSize=3)
# p0 = cv2.goodFeaturesToTrack(gray, mask=None, **feature_params)
# p0_1d = p0[:, 0, :].flatten()
# p0_tuples = utils.xy_vec_to_tuples_list(p0_1d)
# utils.cvshow("goodFeaturesToTrack", q3.mark_points(image.copy(), p0_tuples))
# Detect faces in the image
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
# https://stackoverflow.com/questions/20801015/recommended-values-for-opencv-detectmultiscale-parameters#answer-20805153
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
if y > int(image.shape[0] / 2):
# Assuming face start at the upper half of the image - TODO - is this a valid assumption?
continue
# Mark face area
cv2.rectangle(image_dup, (x, y), (x + w, y + h), (0, 255, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = image_dup[y:y + h, x:x + w]
# Find all facial features:
# features_in_face = [(p0_1d[i], p0_1d[i + 1]) for i in range(0, p0_1d.size, 2) if is_point_in_rect(x, y, w, h, p0_1d[i],p0_1d[i + 1])]
# Extract relevant facial features
# Eyes
features_in_face_filtered = list()
eyes = eyeCascade.detectMultiScale(roi_gray,
scaleFactor=1.1,
minNeighbors=6,
minSize=(20, 20))
eye_cnt = 0
ex_prev = 0
eye_top = list()
eye_bottom = list()
for (ex, ey, ew, eh) in eyes:
if ey < 15:
continue
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh),
(0, 255, 0), 2)
# Take center of bounding box - works better than feature filtering
eye_feature = (x + ex + int(ew / 2), y + ey + int(eh / 2))
# Left eye always first in list
if ex > ex_prev:
features_in_face_filtered.append(eye_feature)
else:
features_in_face_filtered.insert(0, eye_feature)
eye_top.append(ey)
eye_bottom.append(ey + eh)
ex_prev = ex
# Detect only 2 eyes
eye_cnt += 1
if eye_cnt == 2:
break
eye_top__max = max(eye_top)
eye_bottom_min = min(eye_bottom)
# Nose
nose = noseCascade.detectMultiScale(
roi_gray
) #, scaleFactor=1.1, minNeighbors=5, minSize=(20, 20))
for (nx, ny, nw, nh) in nose:
if ny - eye_bottom_min >= 10 or ny <= eye_top__max:
# Assumption that nose upper border is very close to eye bottom border
continue
cv2.rectangle(roi_color, (nx, ny), (nx + nw, ny + nh),
(0, 255, 0), 2)
# Take center of bounding box - works better than feature filtering
nose_feature = (x + nx + int(nw / 2), y + ny + int(nh / 2))
features_in_face_filtered.append(nose_feature)
break
if len(nose) == 0 or len(features_in_face_filtered) < 3:
# If no nose was detected, take nose coordinates of last nose
print("Nose wasn't detected in frame " + str(i) +
" - taking last detected nose point")
features_in_face_filtered.append(nose_feature)
no_nose_cnt += 1
# Mouth
# mouth = mouthCascade.detectMultiScale(roi_gray) #
# for (mx, my, mw, mh) in mouth:
# if my < int(h/2):
# # Mouth - Assuming in lower half of mouth to filter false detections - TODO - is this a valid assumption?
# continue
# cv2.rectangle(roi_color, (mx, my), (mx + mw, my + mh), (0, 255, 0), 2)
# features_in_mouth = [feature for feature in features_in_face if is_point_in_rect(x+mx, y+my, mw, mh, feature[0],feature[1])]
# if len(features_in_mouth) > 0:
# features_in_face_filtered.append(features_in_mouth[0])
# break
# Plot filtered features on frame
# utils.cvshow("goodFeaturesToTrack", q3.mark_points(image_dup, features_in_face))
q3.mark_points(image_dup, features_in_face_filtered)
if len(features_in_face_filtered) < 3:
# print("Found {0} faces, {1} eyes, {2} nose and {3} mouth in and total of {4} facial features in frame {5}! ".format(len(faces), len(eyes), len(nose), len(mouth), len(features_in_face_filtered), i))
print(
"Found {0} faces, {1} eyes and {2} nose in and total of {3} facial features in frame {4}! "
.format(len(faces), len(eyes), len(nose), len(mouth),
len(features_in_face_filtered), i))
# Shoulders
# upperBodyCascPath = haar_dir + "/haarcascade_upperbody.xml"
# upperBodyCascade = cv2.CascadeClassifier(upperBodyCascPath)
# upperBody = upperBodyCascade.detectMultiScale(gray , scaleFactor=1.01) #, minNeighbors=5, minSize=(30, 30))
# for (bx, by, bw, bh) in upperBody:
# # Mark body area
# cv2.rectangle(image_dup, (bx, by), (bx + bw, by + bh), (0, 255, 0), 2)
cv2.imwrite(haar_dir_results + '/' + str(i) + '.jpg', image_dup)
# cv2.imshow("Faces found", image_dup)
# cv2.waitKey()
frames_features.append(features_in_face_filtered)
print("Nose wasn't detected in " + str(no_nose_cnt) + " frames")
print("number of bad faces is: " + str(bad_frames_cnt) +
" and very bad faces is: " + str(very_bad_frames_cnt))
return frames_features
"Frame #n: Right shoulder (Point 2), Right elbow (Point 3), Left shoulder (Point 5), Left elbow (Point 6) \n"
)
for i in range(len(frame_list)):
# Find joints
frame = frame_list[i]
# Based on MPI DB
# keypoints, skeleton = OpenPoseImageREL.get_pose(frame.copy(), "MPI")
# cv2.imwrite(joints_dir_results + '/mpi/' + str(i) + '_keypoints.jpg', keypoints)
# Based on COCO DB
keypoints, skeleton, shoulder_points = OpenPoseImageREL.get_pose(
frame.copy(), "COCO")
cv2.imwrite(joints_dir_results + '/coco/' + str(i) + '_keypoints.jpg',
keypoints)
# Write result to file
RS = shoulder_points[0] if (len(shoulder_points) > 0) else (-1, -1)
RE = shoulder_points[1] if (len(shoulder_points) > 1) else (-1, -1)
LS = shoulder_points[2] if (len(shoulder_points) > 2) else (-1, -1)
LE = shoulder_points[3] if (len(shoulder_points) > 3) else (-1, -1)
file.write("%s: %s, %s, %s, %s \n" % (i, RS, RE, LS, LE))
file.close()
if __name__ == "__main__":
frame_list = utils.get_all_frames(utils.get_pwd() + '/our_data/ariel.avi')
# features = detect_features(frame_list)
detect_joints(frame_list)
import cv2
from hw4 import utils
haar_dir = utils.get_pwd() + "/haar_xmls"
def detect_face_features(image):
faces = detect_face_features(image)
for (x, y, w, h) in faces:
eyes = detect_eyes()
def detect_faces(image):
faceCascPath = haar_dir + "/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(faceCascPath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
filtered_faces = list()
for (x, y, w, h) in faces:
if y > int(image.shape[0] / 2):
# Assuming face start at the upper half of the image - TODO - is this a valid assumption?
continue
filtered_faces.append((x, y, w, h))
return filtered_faces